A Review on Damage and Rupture Modelling for Soft Tissues

Novel Insights into the Aortic Mechanical Properties of Mice Modeling Hereditary Aortic Diseases

OBJECTIVE

 Hereditary aortic diseases (hADs) increase the risk of aortic dissections and ruptures. Recently, we have established an objective approach to measure the rupture force of the murine aorta, thereby explaining the outcomes of clinical studies and assessing the added value of approved drugs in vascular Ehlers-Danlos syndrome (vEDS). Here, we applied our approach to six additional mouse hAD models.

MATERIAL AND METHODS

 We used two mouse models (Fbn1C1041G and Fbn1mgR ) of Marfan syndrome (MFS) as well as one smooth-muscle-cell-specific knockout (SMKO) of Efemp2 and three CRISPR/Cas9-engineered knock-in models (Ltbp1, Mfap4, and Timp1). One of the two MFS models was subjected to 4-week-long losartan treatment. Per mouse, three rings of the thoracic aorta were prepared, mounted on a tissue puller, and uniaxially stretched until rupture.

RESULTS

 The aortic rupture force of the SMKO and both MFS models was significantly lower compared with wild-type mice but in both MFS models higher than in mice modeling vEDS. In contrast, the Ltbp1, Mfap4, and Timp1 knock-in models presented no impaired aortic integrity. As expected, losartan treatment reduced aneurysm formation but surprisingly had no impact on the aortic rupture force of our MFS mice.

CONCLUSION

 Our read-out system can characterize the aortic biomechanical integrity of mice modeling not only vEDS but also related hADs, allowing the aortic-rupture-force-focused comparison of mouse models. Furthermore, aneurysm progression alone may not be a sufficient read-out for aortic rupture, as antihypertensive drugs reducing aortic dilatation might not strengthen the weakened aortic wall. Our results may enable identification of improved medical therapies of hADs.

Three-year follow-up of the grip concept: an open, prospective, observational registry study on biomechanically calculated abdominal wall repair for complex incisional hernias

PURPOSE

We studied the effectiveness of biomechanically calculated abdominal wall reconstructions for incisional hernias of varying complexity in an open, prospective observational registry trial.

METHODS

From July 1st, 2017 to December 31st, 2020, four hospitals affiliated with the University of Heidelberg recruited 198 patients with complex incisional hernias. Hernias were repaired using biomechanically calculated reconstructions and materials classified on their gripping force towards cyclic load. This approach determines the required strength preoperatively based on the hernia size, using the Critical Resistance to Impacts related to Pressure. The surgeon is supported in reliably determining the Gained Resistance, which is based on the mesh-defect-area-ratio, as well as other mesh and suture factors, and the tissue stability. Tissue stability is defined as a maximum distension of 1.5 cm upon a Valsalva maneuver. In complex cases, a CT scan of the abdomen can be used to assess unstable tissue areas both at rest and during Valsalva's maneuver.

RESULTS

Larger and stronger gripping meshes were required for more complex cases to achieve a durable repair, especially for larger hernia sizes. To achieve durable repairs, the number of fixation points increased while the mesh-defect area ratio decreased. Performing these repairs required more operating room time. The complication rate remained low. Less than 1% of recurrences and low pain levels were observed after 3 years.

CONCLUSIONS

Biomechanical stability, defined as the resistance to cyclic load, is crucial in preventing postoperative complications, including recurrences and chronic pain.

Experimental and Numerical Investigation of the Fracture Behavior of Extruded Wood-Plastic Composites under Bending

The ability of wood-plastic composites (WPCs) to withstand various loads and resist plastic failure is attracting more and more interest due to the global increase in demand for WPCs by over 6 million tons per year. Among the most important and innovative research methods are those based on fracture mechanics-their results enable material designers to optimize the structures of these hybrid polymer composites at the nano, micro and macro levels, and they allow engineers to more accurately evaluate and select functional, sustainable, long-lasting and safe product designs. In this study, standard single-edge notched bending (SENB) tests were used to analyze the fracture toughness of two different extruded WPCs along the longitudinal (L) and transverse (T) directions of extrusion. In addition to their resistance to crack propagation, critical fracture criteria, initial contact stiffness, fracture parameters and fracture surfaces, the mechanical properties of these composites were also investigated. The results showed that WPC-A coded composites withstood higher loads until failure in both directions compared to WPC-B. Despite the larger data spread, both types of composites were more resistant to crack propagation in the T direction. Mode II of crack propagation was clearly visible, while mode III was not as pronounced. The experimental results and the numerical finite element (FE) model developed up to 58% of the maximum load correlated well, and the obtained deformation curves were best approximated using cubic equations (R2 > 0.99). The shear stress zone and its location, as well as the distribution of the equivalent stresses, had a major influence on crack propagation in the fracture process zone (FZP).

Nonlocal damage evaluation of a sigmoid-based damage model for fibrous biological soft tissues

The comprehension and modeling of the mechanical behavior of soft biological tissues are essential due to their clinical applications. This knowledge is essential for predicting tissue responses accurately and enhancing our ability to compute the behavior of biological structures and bio-prosthetic devices under specific loading conditions. The current research is centered on modeling the initiation and progression of soft tissues damage, which typically exhibit intricate anisotropic and nonlinear elastic characteristics. For this purpose, the following study presents a comparative analysis of the computational performance of two distinct damage modeling techniques. The first technique employs a well-established damage model, based on a piece-wise exponential damage function as proposed by Calvo et al. (Int J Numer Methods Eng 69:2036-2057, 2007. https://doi.org/10.1002/nme.1825 ). The second approach adopts a sigmoid function, as proposed by López-Campos et al. (Comput Methods Biomech Biomed Eng 23(6):213-223. https://doi.org/10.1080/10255842.2019.1710742 ). The aim of this study is to verify the validity of the López-Campos sigmoid-based damage model to be used in finite element simulation, the implementation of which is unknown. For this proposal, both models were implemented within a commercial Finite Element software package, and their responses to local and non-local damage algorithms were assessed in depth through two standard benchmark tests: a plate with a hole and a ball burst. The results of this study indicate that, for a wide range of cases, such as in-plane stresses, out-plane stresses, stress concentration and contact, all over large displacement conditions, the López-Campos damage model shows a good response to non-local algorithms achieving mesh independence and convergence in all these cases. The results obtained are in line with those obtained for the Calvo's damage model, showing, in addition, larger deformations under in-plane stress and stress concentration conditions and a lower number of iterations under out-plane stress and contact conditions. Consequently, the López-Campos' damage model emerges as a valuable and useful tool in the field of mechanical damage research in biological systems.

Real-time non-uniform surface refinement model for lung adenocarcinoma surgery

Soft tissue models play a crucial role in virtual surgery. However, most existing methods use uniform meshes and overall refinement to construct inhomogeneous soft tissues for virtual lungs. This leads to a complex computation and poor model realism. Therefore, a real-time non-uniform surface refinement model (RNSM) for lung adenocarcinoma surgery is proposed in this paper. First, to better describe the inhomogeneous soft tissues, the tetrahedra are subdivided to different degrees depending on their densities, which reduce the model's complexity while ensuring accuracy. Second, to improve the model accuracy, the model surface is subdivided using the Loop subdivision method. Finally, an optimal algorithm based on deformation radius is designed to enhance the deformation in real-time, in which a linear attenuation method of physical quantities is used to simulate the deformation of the weak deformation regions directly, and the finite element method (FEM) is used for the strong deformation regions. The experimental results show that the model is more accurate and faster than the existing soft tissue models for lung adenocarcinoma surgery simulation.

Low-intensity pulsed ultrasound in obstetrics and gynecology: advances in clinical application and research progress

In the past decade, research on ultrasound therapy in obstetrics and gynecology has rapidly developed. Currently, high-intensity ultrasound has been widely used in clinical practice, while low-intensity ultrasound has gradually emerged as a new trend of transitioning from pre-clinical research to clinical applications. Low-intensity pulsed ultrasound (LIPUS), characterized by a non-invasive low-intensity pulse wave stimulation method, employs its non-thermal effects to achieve safe, economical, and convenient therapeutic outcomes. LIPUS converts into biochemical signals within cells through pathways such as cavitation, acoustic flow, and mechanical stimulation, regulating molecular biological mechanisms and exerting various biological effects. The molecular biology mechanisms underlying the application of LIPUS in obstetrics and gynecology mainly include signaling pathways, key gene expression, angiogenesis, inflammation inhibition, and stem cell differentiation. LIPUS plays a positive role in promoting soft tissue regeneration, bone regeneration, nerve regulation, and changes in cell membrane permeability. LIPUS can improve the treatment benefit of premature ovarian failure, pelvic floor dysfunction, nerve damage caused by intrauterine growth restriction, ovariectomized osteoporosis, and incomplete uterine involution through the above biological effects, and it also has application value in the adjuvant treatment of malignant tumors such as ovarian cancer and cervical cancer. This study outlines the biological mechanisms and applications of LIPUS in treating various obstetric and gynecologic diseases, aiming to promote its precise application and provide a theoretical basis for its use in the field.

Preparation and In Vitro Characterization of Magnetic CS/PVA/HA/pSPIONs Scaffolds for Magnetic Hyperthermia and Bone Regeneration

Conventional bone cancer treatment often results in unwanted side effects, critical-sized bone defects, and inefficient cancer-cell targeting. Therefore, new approaches are necessary to better address bone cancer treatment and patient's recovery. One solution may reside in the combination of bone regeneration scaffolds with magnetic hyperthermia. By incorporating pristine superparamagnetic iron oxide nanoparticles (pSPIONs) into additively manufactured scaffolds we created magnetic structures for magnetic hyperthermia and bone regeneration. For this, hydroxyapatite (HA) particles were integrated in a polymeric matrix composed of chitosan (CS) and poly (vinyl alcohol) (PVA). Once optimized, pSPIONs were added to the CS/PVA/HA paste at three different concentrations (1.92, 3.77, and 5.54 wt.%), and subsequently additively manufactured to form a scaffold. Results indicate that scaffolds containing 3.77 and 5.54 wt.% of pSPIONs, attained temperature increases of 6.6 and 7.5 °C in magnetic hyperthermia testing, respectively. In vitro studies using human osteosarcoma Saos-2 cells indicated that pSPIONs incorporation significantly stimulated cell adhesion, proliferation and alkaline phosphatase (ALP) expression when compared to CS/PVA/HA scaffolds. Thus, these results support that CS/PVA/HA/pSPIONs scaffolds with pSPIONs concentrations above or equal to 3.77 wt.% have the potential to be used for magnetic hyperthermia and bone regeneration.

Variational Autoencoders-BasedSelf-Learning Model for Tumor Identification and Impact Analysis from 2-D MRI Images

Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist's task and the practitioners more comfortable assessing the tumor's progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result.